Random Forests for Forest Ecology
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".
Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 8507
Special Issue Editor
Special Issue Information
Dear Colleagues,
Random Forests was first proposed by Breiman (2001) and was subsequently developed by Breiman and Adele Cutler. Their code formed the basis of the Salford Systems (now Minitab) and R (Liaw and Wiener 2002) implementations of Random Forests. Due to its highly predictive accuracy for both classification and regression, without any tuning, its novel model selection algorithm, and its ability to perform both imputation and unsupervised learning, Random Forests has rapidly become one of the most popular and widely used machine/statistical learning methodologies. Random Forests is now implemented in most statistical packages and programming languages, including SAS and Python. Development and analysis of the algorithms that make up Random Forests continue with notable contributions, including Survival Random Forests (Ishwaran et al. 2008) and conditional variable importance for Random Forests (Strobl et al. 2008).
Random Forests has been applied to data in almost every area of research in which regression, survival analysis, and classification are used. One of the earliest published papers (Prasad et al. 2006) was an application of Random Forests to forest data. For this Special Issue, we are seeking papers in all areas of application of Random Forests to forest and remote sensing data, including comparison with other methods. We are particularly interested in novel uses of prediction, model selection, and visualization of relationships between response and predictor variables.
Prof. Richard Cutler
Guest Editor
References
- Breiman, L. (2001). Random Forests. Machine Learning 45 5—32.
- Iswaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer (2008). Survival Random Forests. Annals of Applied Statistics 4(3):841—860.
- Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3):18—22.
- Prasad, A. M., L. R. Iverson, and A. Liaw. (2006). Newer Classification and Regression Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9:181—199.
- Strobl, C., A-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis. (2008). Conditional Variable Importance for Random Forests. BMC Informatics 9(307).
Manuscript Submission Information
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Keywords
- Random Forests
- Machine/statistical learning
- Prediction
- Model selection
- Partial dependence
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